Dashboard Design: Bullet Graph vs. Bar Chart

We invest a lot of time and energy communicating our research, because unless we can effectively communicate our findings they are useless.  When the goal is to communicate the most valuable information with the least amount of ink that can be understood with the least amount of effort.  For your reference, our major influences are Deirdre McCloskey on writing, Stephen Few on dashboard design, and Edward Tufte on data visualization.

Recently, CAN conducted a customer satisfaction survey for the Georgia Regional transportation Authority.  In addition to developing, deploying and analyzing the customer survey, CAN went above and beyond to improve how GRTA reported the results of their annual survey.  In this post, I will explain why we used a modified bullet graph instead of a bar chart to answer the business question.

The purpose of the graph is to help answer the business question of how does GRTA compare to two competitors across 17 different metrics.  While GRTA needs to continually improve, for the purpose of  answering the business question the exact score was not important, but instead the difference between each competitor and compared to others how does GRTA score.  Comparing each company by metric was the main influence behind the design on CAN’s graph.

The Original Graph


 
 

The CAN Graph


– In the original graph, the bold vertical lines focus the viewer how each metric scored, by encouraging the eyes to go up and down.  In the CAN graph, the light gray horizontal lines encourage the eyes to travel left and right to compare each companies performance.  Also, we used light gray lines so that we did not dominate the graph with supporting data.
– In the original graph, there is no simple way to show the spread between the different competitors, besides comparing each line together.  However, it important to know how competitive each metric is when answering the business question.  When designing the CAN Graph, we darkened a length of the light gray horizontal lines to show the minimum and maximum score on the service quality index.  This
– In the original graph, using four different colors made it difficult to make a memorable distinction between each company, take up an unnecessary amount of space, and impossible for color blind (10% of males) to make distinctions.  Using different shades of gray CAN made it easy for everyone, including the colorblind, to distinguish between different companies.  In addition to adding an additional way to differentiate between companies, using different shapes allowed for better distinction when multiple companies score close to each other.
– In the original graph, the overall low graphical quality such as broken vertical lines, faded colors and pixilated font created an unnecessary distraction, and reduce the credibility of the results.  While this might seem petty, producing graphs that are crisp and well designed help develop trust with the audience.  In the CAN Graph, we produced the entire graph in black and white, so that the report can easily be reproduced on either a color or black and white printer.
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November 29, 2011 News No comments yet

Dashboard Design: Teaching Strategic and Analytical Thinking

At CAN, we exist to provide our clients with leading edge methodologies that are both effective and easy to use.  This requires that we constantly learn about new tools and techniques, and hone a fine edge on the ones we keep to provide to our clients.

Previously on our blog, we have discussed the application of dashboards and aspects of dashboard design that facilitate rapid perception by the human brain.  How about using dashboards as a way to teach users a way of thinking?  In this blog, we will discuss using dashboards to promote strategic thinking through guided analysis.
One of our clients approached CAN with the following predicament.  Their enterprise operates nationwide with several districts responsible for operations within their unique geographic region.  Every year, the strategic planning division would produce a thick binder reviewing each districts market forecasts, opportunities, and past performance.  The intent was to assist the non-technical managers and business development of each district to think about trends in the market and industry to get more sales.  Although very well produced and full of useful information, these binders acted mostly as a reference and did little to encourage analysis by the end-user.
Our solution was to use the same information used to build the binders and create views using Tableau.  At first, these views replicated the familiar visualizations found in binders with an added level of interaction.  Then, we started to add new data sources into the existing information.  We connected industry forecasts, census data, economic indicators, past performance and connected all this functionality to a dashboard where the end user is able to bring in these factors at their command.  Populating the dashboard with the raw materials required for analysis, is the first stage.
The second stage is defining the business questions that the users need to answer to run their business.  We interviewed the executives on the strategic planning team and in several of the district offices to define what the most important business questions they needed to answer to run their business.  Instead of providing managers of each district with binders that pushed facts and figures at them, we created a work book of questions that needed to be answered and how the answers could be applied to running their district.
The third stage is doing most, not all, of the users’ work for them.  What I mean by this is producing dashboards that are 90% completed for the types of questions the user will want to answer.  Our goal is to support the user in asking questions and getting answers, not simply handing them the answers or making them build their own dashboards.  So, we build pre-made views for them.  For example, one aspect of our client’s business functions was closely related to population growth.  We produced a dashboard that integrated population growth figures for the past several years with our client’s historical sales figures and billable hours.  The district manager, interested in staffing requirements, can population changes across the region with his current staffing and identify where adjustments and hiring are likely to take place.
In designing guided analysis, the bottom line is producing dashboards that solve the business question that users need to answer.  This requires that the designers understand the purpose of each dashboard, how it will be used, and what the user intends to get out of it.  If your goal is to achieve data-driven decisions from non-technical managers, you must design so that the user is on the right track with the controls, but ultimately require their interaction and thinking to reach the outcome.
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November 28, 2011 News No comments yet

Be First and Influence the Purchasing Process

I just got off the phone with a friend of mine in the Hotel business in Omaha. It was an interesting conversation because she had lost a sale because her wall in the event space wasn’t green. Not environmentally green, I mean she didn’t get the job because her space wasn’t the color green. She was trying to figure out why someone would choose an event space based on the color. I explained to her that someone had  influenced the purchase process.

I imagine the conversation went something like this: (more…)

November 25, 2011 News No comments yet

Cold Calling Works Again

Cold calls used to work, then they didn’t and now they work again. I used to agree with most people, that cold calls do not work. In fact, I established my sales career on referral networking. However, I have rediscovered the power of cold calling and how to do it effectively.  Networking is still important, but now I don’t have to wait around hoping for referrals.
Before the internet cold calling was effective because talking to salespeople was the most effective way for most people to learn about new products and services. As long as you had a good product, solid reputation and solid sales skills you could be successful. If someone wasn’t willing to take the time to listen to your sales pitch then they weren’t open to learning about the latest and greatest innovations that could transform their company.
However, the Internet made cold calling ineffective. It provided a more effective alternative to talking with salespeople that typically didn’t value people’s time and attention. Now, people had the ability to learn about new product and didn’t need someone to “sell it to them”.
Cold calling no longer worked because people no longer had problems to solve that they couldn’t solve using the Internet. They didn’t want a salesperson to create a problem. They certainly did not have time to listen to another sales pitch. If they had a problem they could solve it themselves, and this essentially took the power away from salespeople.
Salespeople transitioned from cold calling to networking and developing referral relationships. This worked because it established trust with prospects, and trust was something that the Internet lacked. The buyer did all the research to find possible solutions to meet their need, and then asked friends for a referral to someone they could trust to make answer a couple questions, provide a recommendation, and take the order. However, it is difficult to build a reliable sales system through networking and referrals, because you are relying on someone else to make the first move and then making sure that you are positioned in cahoots with the first person that they would ask for advice.
What cold calling and the Internet had allowed buyers to do is find products and services that they had the need, willingness and resources to purchase. The secrete is talking to the right people at the right time. With the right timing cold calling can be effective again, and sales people can once again activity take control of their pipeline.
Once we realized that timing was the secret, CAN set out on a mission to get our timing right. How could we build a system that would allow sales people to find leads when they had the need, willingness and resources to purchase?  The solution is Predictive Lead Generation. Predictive Lead Generation allows you to build a detailed profile of your ideal client that identifies what factors trigger prospects to have the need, willingness and resources to purchase your product, and find leads that have the attributes of someone who is ready to purchase, and find supporting evidence you need to successful call and build trust.  Instead of calling 100 people to get one person that is interested in your product, CAN is able to give you a list of 10 people.  You still have to have a great product and solid sales pitch, but Predictive Lead Generation can help you focus on talking to the right people.
We have been using Predictive Lead Generation internally for four years. Before Predictive Lead Generation our sales team used to spend the entire week attending networking events hoping to snag a solid lead, and make up excuses about how sales is all about luck and can’t produce reliable results. Now, our sales team is focused on building relationships with the right people, and I am confident that my team will be able to deliver each month.
I encourage you to use Predictive Lead Generation to put cold calls back into your arsenal. If you want to try out cold calling search the Internet and find a company you decide needs, wants and has the resources to purchase what you sell, spend 10 minutes learning about the person you are calling, and then call someone who actually needs what you are selling, and will be glad you called. If that produces results, then you might be a good candidate for CAN’s Predictive Lead Generation system. While referrals may always be the easiest phone call, cold calls are now some of the most effective.

November 23, 2011 News No comments yet

Grant's Interview with NebraskaEntrepreneur.com

NebraskaEntrepreneur.com recently ask readers to nominate entrepreneurs and companies that they thought had a great idea or story to share.  Ali Schwanke from Leadership Resources nominated Contemporary Analysis.  Three Pillars Media produced this great video about CAN’s product development strategy and the difference between small business owners and entrepreneurs.

Contemporary Analysis was founded on the premise that there is always a better way. In fact, we exist to help you find better ways to work smart. We do this using a methodology called predictive analytics.
Predictive analytics involves collecting data about your business and customers, and then applying theory and math to build simple systems to help you work more effectively and efficiently.
Our systems are tailored to fit your company no matter how big or small or what industry you are in. We have built simple systems for fast-growing technology companies, Fortune 500 companies as well as small companies in a variety of industries including community colleges, insurance companies, software companies and engineering firms.

November 21, 2011 News 2 comments

Identifying Your Target Market

When I started my sales career, my philosophy was to let anyone who needed my product buy from me. It worked. I was one of the more successful young salespeople and I exceeded my quota month after month. My target market was “anyone and everyone”, and it seemed to be working. However, I was unknowingly limiting my future success.

After hearing from several of peers who had been in sales for years that they wish they could start over and not have the bottom 10-20% of their book of business, I conducted research on my book of business. My research showed that 75% of my time was spent on customer service issues with only 15% of my clients. Furthermore, looking at who that 15% were, the study found that 75% of those were my lowest yielding profit margin clients. In addition to my failed attempts at asking for referrals to anyone, it was obvious that I needed to invest in identifying and focusing my sales efforts on my target market. Learn how CAN helped the Admissions department at a University focus on recruiting the right students. 
Once I identified my target, two things happened: I started to receive referrals when I asked for them, and I was finally able to apply a strategy to my sales efforts. I no longer felt that I had to get everyone as a client, in fact, I started turning away people who were not my target market. Low and behold, gone were the  price chasers, time wasters, and no-money makers that wasted so much time.

I used the following strategy to identify my target market, so that I could start selling smart:

First, I identified the events that caused someone to buy my product.

In my case it was a major life change.  Things like buying a new home, getting married, having children, and changing jobs.

Second, I identified the characteristics of the people were that were experiencing these events.

In my case it was 20-30 year olds that had graduated college, had lived in apartments for 4 to 5 years, had a job were they made $50,000 or more  a year, and had met their fiancés but had yet to marry them.

Third, I identified where my target market spends time.

In my case, my target market spent time at first time home buyer classes, professional certification training classes, marriage classes, gyms, and trendy restaurants. This provided my advertising with focus, also I started to have meetings and work at coffee shops and restaurants where I could meet my target market.

Lastly, I identified the people who were around people who needed my product.

In my case, these were real estate agents, mortgage brokers, ministers, trendy restaurant owners, lawyers, and headhunters. This step allowed me to know where I needed to spend my time networking and which events I needed to attend. All of a sudden I knew where I needed to be and who I needed to meet at each event.
Now when people are outside my target market, I can recognize when they might be one of the bottom 15% of my market that would suck up 75% of my customer service time. I can weigh the cost and benefit and decide if I should bring them on as a client, or refer them to someone who might be able to serve their needs better.
Let me know if you would like help identifying your target market, or using predictive analytics to find people who fit your profile and are looking to purchase.
Learn more about how CAN helped a online university identify their target market.

October 31, 2011 News 6 comments

Using Business Cards

Good networkers typically collect between 3 to 5 business cards for every hour they invest at a networking event.  It is important to have a system to process the business cards you collect at networking events, because while you might be able to process 3 to 5 business cards a day, anything over that requires a systematic way to keep up with connections until they become friends and clients.
Learn how we applied predictive analytics to CRM & accounting data to identify who was 60 to 80% likely to enroll at a Top 10 Online University. 
Step 1: The first thing that I do is prioritize business cards based on the conversations that I have had with people.  I start by throwing away the business cards of people that handed me their card, but that we failed to have a conversation.  The reason that I throw those business cards out is because I don’t know anything about that person and their business, and they choose not to take the time to learn anything about me and my business.  For people that I did have a conversation with I write down details about that person in my CRM (see Step 2). (more…)

October 24, 2011 News 2 comments

Why Business Cards Don't Work

At any networking event there are people that you want to avoid because they cling to you, and prevent you from meeting new people. Usually an effective technique is excusing oneself to refill your drink or using the restroom. However, recently I was resought out after politely excusing myself. “What was i to do now?” I had gone to the restroom and I had a full plate of food. The solution, I handed them my business cards and told them to call me.
This was the perfect solution. By handing them my business card I was able to walk away and continue to network with other people. Most likely the person will never call me, because 99% of the time, people NEVER CALL. If he does call then he will probably be a lead and not a clinger. While I needed to continue to network, I can take the time in a one-to-one meeting if he is interested enough to continue the call. (more…)

September 21, 2011 News 3 comments

Presenting Predictive Analytics

The nature of forecasting the future makes presenting predictive analytics unique and challenging.  There is no flashy server or dashboard that will make presenting analytics any easier.  There is only a model that tells a story about the future of users’ business, customers, non-customers and competitors.  While models are very valuable they are not your typical business intelligence artifacts.  To produce a meaningful return on investment you need to translate the details of the story into results that can be applied to a specific business question.

While you can not replace sound scientific and statistical methodology, CAN has found that users don’t care about a model’s Durbin-Watson, standard error, or R², or they are not familiar enough to properly understand the statistical nuances.  The key to proving that a model works and getting political support required for implementation is to ask the experts if the story the model tells reflects reality.  It is also valuable to prove that a model works by letting the prove itself over time.

It is important to note that predictive models typically do not provide solutions to business questions, but instead often offer incomplete answers and important insights.  When presenting predictive analytics your audiences expectations should be set on becoming less wrong, instead of finding the perfect solution.  CAN finds that users appreciate our philosophy of Less Wrong.  While it seems counter intuitive, our lack of hubris builds confidence in our models and sets realistic expectations.  The basic principle behind, Less Wrong is that in business winners are not right, they are simply less wrong.  There are no perfect answers in complex sciences, such as data science and predictive analytics, only less wrong answers.  The goal is to reduce the uncertainty of making the wrong decisions, not thinking uncertainty can be eliminated.

In conclusion when presenting predictive analytics don’t be afraid to kill your darlings.  If you can not justify an element of your presentation get rid of it.  This will help you focus your presentation, your audience will listen and the results of your hard work building predictive model will get implemented.

September 19, 2011 News No comments yet

Applications of Predictive Analytics

The following are some examples of how predictive analytics can be applied in financial services, retail and manufacturing.  This list is not comprehensive, but it provides some interesting applications.
In the financial services the cost of making the right decisions provides marginal benefits, while making the wrong decisions can have significant costs.  Most applications of predictive analytics in the financial services industry help companies avoid making the wrong decisions. For example, credit card companies are able to determine who is most likely to default on their credit cards in the next 6 months by applying predictive analytics to customers purchases and demographics.
In retail understanding customers is essential to success.  Retailers have provided customized shopping experiences by using predictive analytics to understand the drivers of profitability, loyalty and activity for each customer segment and develop specific campaigns for each segment.  This has allowed retailers to wow customers with personalized services while scaling and keep prices competitive.  Predictive analytics have helped both offline and online retailers determine which products to carry, optimize marketing plans, and develop promotional and loyalty programs.  Imagine only offering effective loyalty promotions to profitable customers at risk of leaving, while avoiding offering discounts to unprofitable or already loyal customers.  Another example is knowing what a customer is most likely to purchase next, so that your staff or website can make informed recommendations.
Manufacturing is about knowing what, how, and how much to produce.  Predictive analytics have helped manufactures manage their supply chain and production schedules by accurately forecasting demand, and have helped manufactures produce goods in the most effective way possible by predicting failure of equipment, monitoring workers, and identifying ways to eliminate inefficiencies.  For example, CAN has helped companies with tens of thousands of sales each month forecast sales within a 10 to 50 units, so that they can optimize production schedules and supply chains.  Also, imagine being able to understand why different managers have different levels of employee turnover, employee injuries, and equipment failure.
Beyond specific applications, predictive analytics has the unique ability to help companies become less wrong, scale decisions and systematized learning.
Less Wrong: The basic idea of Less Wrong is that in business, and almost anything in life, you can never be perfectly right, but you can be less wrong and by striving to continually become less wrong you get closer and closer to being right.  By using predictive analytics you will not get the perfect answer, but you can determine what is happening, what most likely happen and most is most likely the right thing to do.  For example it might be really nice to know exactly what commodities prices will be in a month, unfortunately this is not possible.  However, using predictive analytics you might be able to predictive with 70% accuracy which direction a commodity price will trend and if this is better than before predictive analytics then it most likely will be worth the investment.  Another example would be picking which commodity would most likely be the best investment in the next 6 months.  The reason that predictive analytics can’t produce exact results is because it is not a simple science, for more read my post on Simple vs. Complex Science.
Scale Decisions: Predictive analytics has the unique potential to allow executives to scale their decision making as organizations and decisions become increasingly more complex with ever thinner margins for error.  Predictive analytics can be used to create a model of the business based on the organization’s data and executives’ theories.  For example an experienced sales manager will be able to determine which sales leads will most likely respond, purchase and be profitable customers, however he or she does not have time to review every lead for a 200 person sales team.  Also, while a sales manager knows what make a lead worth pursuing, he or she most likely finds it difficult to communicate the rules and criteria to their team.  Using predictive analytics a sales manager can develop a model to score incoming sales leads.  This model can be coded directly into the company’s customer relationship management (CRM) system so that leads are scored as soon as they are entered into the system.
Systematized Learning: In the future, profits will be directly related to your company’s rate of metabolizing new knowledge, as opposed to renting out existing knowledge.  Predictive analytics can increase your company’s ability to metabolize new knowledge by continually studying the data produced by your company, customers, non-customers and competitors to find important patterns that will impact your business.  For example, predictive analytics can help executives identify when customers stop responding to a certain campaign and why.
While every company can benefit from becoming analytical, like any other tool, predictive analytics can not fix anything.  However, it is most certainly the next step in the evolution of business intelligence.  If properly applied, predictive analytics has the potential to help businesses work smart. Read a related post on when to and not to apply predictive analytics.

September 16, 2011 News 1 comment
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